Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression

Adil O Khadidos; Khaled H Alyoubi; Shalini Mahato; Alaa O Khadidos; Sachi Nandan Mohanty

doi:10.1016/j.neulet.2023.137313

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression

Neurosci Lett. 2023 Jul 13:809:137313. doi: 10.1016/j.neulet.2023.137313. Epub 2023 May 29.

Authors

Adil O Khadidos¹, Khaled H Alyoubi², Shalini Mahato³, Alaa O Khadidos⁴, Sachi Nandan Mohanty⁵

Affiliations

¹ Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Electronic address: akhadidos@kau.edu.sa.
² Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia. Electronic address: kalyoubi@kau.edu.sa.
³ Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Ranchi, Jharkhand, India. Electronic address: shalinimahato@iiitranchi.ac.in.
⁴ Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Electronic address: aokhadidos@kau.edu.sa.
⁵ Department of Computer Science & Engineering, Vardhaman College of Engineering(Autonomous), Hyderabad, India. Electronic address: sachinandan09@gmail.com.

PMID: 37257682
DOI: 10.1016/j.neulet.2023.137313

Abstract

Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicide. Electroencephalogram (EEG) signals can be used to diagnose depression using machine learning techniques. The dataset studied is public dataset which consists of 30 healthy people and 34 depression patients. The methods used for detection of depression are Decision Tree, Random Forest, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (Bi-LSTM), Gradient Boosting, Extreme Gradient Boosting (XGBoost) along with band power. Among Deep Learning techniques, CNN model got the highest accuracy with 98.13%, specificity of 99%, and sensitivity of 97% using band power features.

Keywords: Band power; Depression; Detrended Fluctuation Analysis (DFA); Temporal region.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Case-Control Studies
Datasets as Topic
Decision Trees
Depression* / diagnosis
Depression* / psychology
Depressive Disorder, Major / diagnosis
Depressive Disorder, Major / psychology
Electroencephalography*
Humans
Machine Learning*
Neural Networks, Computer
Random Forest